A review on the young history of the wind power short-term prediction

By Alexandre Costaa, Antonio Crespob, J. Navarroa, Gil Lizcanoc, Henrik Madsend, Everaldo Feitosae. 2008

678 citations recorded by the source corpus.

This paper makes a brief review on 30 years of history of the wind power short-term prediction, since the first ideas and sketches on the theme to the actual state of the art on models and tools, giving emphasis to the most significant proposals and developments. The two principal lines of thought on short-term prediction (mathematical and physical) are indistinctly treated here and comparisons between models and tools are avoided, mainly because, on the one hand, a standard for a measure of performance is still not adopted and, on the other hand, it is very important that the data are exactly the same in order to compare two models (this fact makes it almost impossible to carry out a quantitative comparison between a huge number of models and methods). In place of a quantitative description, a qualitative approach is preferred for this review, remarking the contribution (and innovative aspect) of each model. On the basis of the review, some topics for future research are pointed out.

Subfields: Aerospace Engineering, Electrical and Electronic Engineering, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/0f71075bab6b9f2e46f004d23999cd5a00310f1b

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Citations in the corpus

Fine tuning support vector machines for short-term wind speed forecasting (2011, methodology): “Short-term prediction of wind speed can be made in the order of several days and also from minutes to hours [5].”
Wind power forecasting : state-of-the-art 2009. (2009, background): “Recent developments in WPF systems are related to the integration of the two paired approaches, the physical and mathematical models [69].”
Forecasting wind power – Modeling periodic and non-linear effects under conditional heteroscedasticity (2016, background): “The persistent forecaster (so-called näıve predictor, P̂χ(l)+k = Pχ(l) , see, e.g., Costa et al., 2008) is suitable to illustrate the improvement of sophisticated forecasting models in direct comparison to this common quasi-standard benchmark.”
A review of combined approaches for prediction of short-term wind speed and power (2014, background): “summarized the widely used short term prediction models following a chronological sequence of publication on the last 30 years of prediction history and pointed out many promising research topics in the same field [27].”
Current status and future advances for wind speed and power forecasting (2014, background): “Research in the area of forecasting wind speed or the power produced by wind farms has been devoted to the development of effective and reliable tools and many different approaches have been proposed and reviewed in [2-13].”
Deep Learning for solar power forecasting — An approach using AutoEncoder and LSTM Neural Networks (2016, background): “The area of short-term forecasting is covered in [3], [5], mid-term forecasting is described in [6], long-term forecasting techniques are described in [7].”
On the market impact of wind energy forecasts (2010, background): “In the case of wind power, production forecasting is a major and rapidly growing research field and has been the topic of countless papers (see e.g Giebel et al., 2003; Costa et al., 2008, for a state of the art review).”
On comparing three artificial neural networks for wind speed forecasting (2010, methodology): “(WECS); wind speed forecasts in the range of hours target the problem of scheduling in a power system, whereas forecasts in the range of days are related with maintenance and resource planning [8,9].”
Very‐short‐term probabilistic forecasting of wind power with generalized logit–normal distributions (2012, methodology): “As a basis for the evaluation of the forecast performance of the methodology that is introduced in the paper, different types of benchmark methods are considered, some of them being reviewed by Giebel et al. (2011), Costa et al. (2008) and Pinson and Madsen (2011).”
Spatio‐temporal analysis and modeling of short‐term wind power forecast errors (2011, background): “over a region with significant installed wind power capacities [1, 2 ]. Even if forecasting methodolo-”
Short-term wind power forecasting in Portugal by neural networks and wavelet transform (2011, background): “The time scales concerning short-term prediction are in the order of some days (for the forecast horizon) and from minutes to hours (for the time-step) [5].”